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  3. Winning a Kaggle Competition in Python
  • 1

    Kaggle competitions process

    Gratuito

    In this first chapter, you will get exposure to the Kaggle competition process. You will train a model and prepare a csv file ready for submission. You will learn the difference between Public and Private test splits, and how to prevent overfitting.

  • 2

    Dive into the Competition

    Now that you know the basics of Kaggle competitions, you will learn how to study the specific problem at hand. You will practice EDA and get to establish correct local validation strategies. You will also learn about data leakage.

  • 3

    Feature Engineering

    You will now get exposure to different types of features. You will modify existing features and create new ones. Also, you will treat the missing data accordingly.

  • 4

    Modeling

    Time to bring everything together and build some models! In this last chapter, you will build a base model before tuning some hyperparameters and improving your results with ensembles. You will then get some final tips and tricks to help you compete more efficiently.

Initializing

Exercicio

Baseline based on the date

We've already built 3 different baseline models. To get more practice, let's build a couple more. The first model is based on the grouping variables. It's clear that the ride fare could depend on the part of the day. For example, prices could be higher during the rush hours.

Your goal is to build a baseline model that will assign the average "fare_amount" for the corresponding hour. For now, you will create the model for the whole train data and make predictions for the test dataset.

The train and test DataFrames are available in your workspace. Moreover, the "pickup_datetime" column in both DataFrames is already converted to a datetime object for you.

Instruções

100 XP
  • Get the hour from the "pickup_datetime" column for the train and test DataFrames.
  • Calculate the mean "fare_amount" for each hour on the train data.
  • Make test predictions using pandas' map() method and the grouping obtained.
  • Write predictions to the file.